...If people actually don't have free will, punishing a natural machine which couldn't be otherwise makes no sense. Harmful people should be removed from society, for society's protection, but not punished, driven into debt, etc.

#society #freedom #freewill #choice #behaviour #nature #einstein #biology #buddhism #complexsystem #complexadaptivesystem #determinism #causality #crime #punishment #prison

Many societies operate on the assumption people have free will/choice over their behaviours, but, if one could view the universe on a large enough scale, both Einstein, some biologists, and Buddhism say the system overall is deterministic, meaning, though complex, people are actually natural automata/machines.

#society #freedom #freewill #choice #behaviour #nature #einstein #biology #buddhism #complexsystem #complexadaptivesystem #determinism #karma #kamma #dependentorigination #causality

Pluralistic: The world has moved on (11 Jun 2026)

https://fed.brid.gy/r/https://pluralistic.net/2026/06/11/lapsarianism/

Things which are connected by a COMMON CAUSE necessarily contain with them some shared causal essence, even if it is homeopathically dilute. On some level, the information which caused me to wonder what my relationship to an event is, is the same as the information that causes me to roll dice about it and is the same that causes me to roll the dice in such a particular way as to produce the outcome of the roll. But can divination really be any more certain than astrology?

On some level, we can never be rid of the facts of a situation, as we are asked to confront by any discussion of the conscious, subconscious, and unconscious mind, or any physiological description of the brain or its chemistry! Nothing simply goes away, and everything contaminates some part of us in ways our bodies amplify on any one of the above levels. Though, perhaps we should think that our minds only register vanishingly small particles of causality in aggregate, such that it is impossible (impractical) to disentangle the web of information which makes up each one's path… but that also, on the whole, each particle causes this web to move one way or another. This aggregation produces feelings and properties all its own.

Our minds can sense tiny things which are incredibly subtle to detect using any other device… Who can say what causal dust bunnies are or are not influencing our perceptions? Who can say the granularity with which our minds and bodies can interpret the minuscule signs all about us?

Линейная регрессия на стероидах: Double Machine Learning для устранения смещений в данных

Любой аналитик знает, что самым надёжным способом проверки гипотез являются рандомизированные контролируемые эксперименты (RCT), или, как их называют в народе — A/B-тесты. На практике часто возникают ситуации, когда провести A/B-тест невозможно — в основном это происходит по этическим или техническим причинам. Однако бывают кейсы, когда рандомизация невозможна потому, что treatment-ом является определённое действие пользователя. Например, treatment-ом может быть оформление платной подписки или отмена бронирования на сервисе. Давайте назовём такой вид воздействия добровольным. В русскоязычном пространстве, и в частности на Хабре, достаточно много статей, посвящённых таким методам Causal Inference, как DiD, PSM и Causal Impact. Тем не менее, к моему удивлению, практически нет статей, посвящённых методам на основе ортогонализации и regression adjustment, хотя, на мой взгляд, именно эти методы являются самыми удобными для оценки эффекта от добровольного treatment-а. Пришло время исправить это недоразумение и разобрать метод Double/Debiased Machine Learning (DML) и Partial Linear Regression для задач Causal Inference!

https://habr.com/ru/articles/1043704/

#causal_inference #machine_learning #abтестирование #причинноследственный_анализ #differenceindifference #psm #causalml #causalimpact #causal_effect #causality

Title: P4: Causal LLM or splitting LLM [2025-09-11 Thu]

- **Economics/Policy:** Assess impacts, clarify causal
pathways, propose policies.
- **Recommendation Systems:** Infer preferences, explain
choices, personalize outputs.

Text of original post: https://try-codeberg.github.io/static/causal-inference.org #causalinference #causality #inference #statistic #observability #llm #reasoning

Title: P3: Causal LLM or splitting LLM [2025-09-11 Thu]

- Lack modular separation, functions are entwined.
- Risk of hallucinated causal links, unreliable for
interventions.
- Formal counterfactuals need extensive external
scaffolding.

**Fields:**
- **Healthcare:** Predict treatment outcomes (reasoner),
explain intervention effects (explainer), recommend
actions (producer). #causalinference #causality #inference #statistic #observability #llm #reasoning

Title: P2: Causal LLM or splitting LLM [2025-09-11 Thu]

Transparency: Modular, explainable // Opaque, explanation
varies
Scalability: Harder (custom/domain) // Easier
(generalizable)
Data types: Model integration required // Prompting in
one model

**LLM Limitations:** LLMs use pattern matching over
explicit causal modeling.
- No explicit causal graphs/mechanisms—only patterns and
correlations. #causalinference #causality #inference #statistic #observability #llm #reasoning

Title: P1: Causal LLM or splitting LLM [2025-09-11 Thu]

needing rigorous, transparent causality.
- Effective for quick prototyping or low-risk tasks where
simulated causal logic suffices.

Causal Inference Neural Networks vs Prompt-Engineered Multimodal LLM
Causality: Explicit, modeled, testable // Pattern-based,
plausible, implicit
Reliability: High (good data/model) // Medium,
errors/hallucinations #causalinference #causality #inference #statistic #observability #llm #reasoning